Unsupervised land classification by self-organizing map utilizing the ensemble variance information in satellite-borne polarimetric synthetic aperture radar

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9489, Issue , 2015, Pages 244-252

  Takizawa, Yuto ^a   Shang, Fang ^a   Hirose, Akira ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Polarimetric synthetic aperture radar; Stokes vector; Unsupervised classification

Indexed keywords

CONFORMAL MAPPING; DISPERSION (WAVES); INFORMATION SCIENCE; POLARIMETERS; RADAR; SELF ORGANIZING MAPS; SPACE-BASED RADAR; SPEECH RECOGNITION; SYNTHETIC APERTURE RADAR;

DISPERSION FEATURES; ENSEMBLE VARIANCES; POLARIMETRIC SCATTERING; POLARIMETRIC SYNTHETIC APERTURE RADARS; STATE CLASSIFICATION; STOKES VECTOR; TWO-STAGE CLUSTERING; UNSUPERVISED CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84952779724     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-26532-2_27     Document Type: Conference Paper

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